Objectives
- Describe the basic principles of measurement uncertainty and error budgets
- Apply these basic principles to the calibration and qualification of analytical instruments
- Allow participants to gain experience in applying the techniques to analytical method validation and method transfer.
- Introduce the Monte Carlo simulation approach for more complex systems
Background
Target Group
Technical Requirements
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Programme
Basics of Measurement Uncertainty
- Definition of measurement and uncertainty
- Analytical Procedures (Methods) as Processes
- Error sources and statistical descriptions
- Accuracy and Trueness under ISO definitions
- ICH Q2(R1) and revision in 2021 to (R2) & Q14
- Approaches to quantifying MU and Error Budgets
- Quantifying uncertainties with a very simple example; a reference standard solution
- Shape of errors
- Statistical assumptions for Error budgets
- Qualification, Calibration & Validation (QCV)
- Experimental Errors
- Calibration example of a UV spectrophotometer
- Wavelength accuracy
- Sensitivity; DL & QL
- Holistic approach of MU
- Case study example; Error Budget Process
- Step 1: Description of the measurement process
- Step 2: Specification of the measurand
- Step 3: Identification of uncertainty sources
- Steps 4 & 5: Quantification and combination of uncertainties
- Monte Carlo Simulation (MCS) modelling
- Principles behind MCS for MU
- Example 1: Making a standard solution of a certified reference material (CRM)
- Example 2: Leachable cadmium determination by Atomic Absorption Spectrometry (AAS)
- Pros & Cons for MCS
- Case Study with interactive workshop
- What is a reportable value?
- OOS, OOE & OOT
- Process performance and process capability
- ICH precision approach
- USP <1010>
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